I've met some people whom believe that the more modern tools (such as Tensorflow) magically require little human input and make it so that you do not need to know/understand the mathematics and statistics. Not sure where they get this idea.
Everyone wants to do machine learning, but nobody seems to want to learn statistics.
I think it's because programming did not require me to learn maths (ok I did learn how to multiply matrices in high school, but I never used it in my life). So my expectation is same with ML.
Those platforms are abstractions so I do not really care how they are implemented. Same way I have not idea how JS really implements objects or sorting. I did one of those crash courses on certain platform, and while there was some stats, I totally did without it. I could probably build a classifier that instead of classifying images of dresses, would classify pillows, curtains or cars. But I did not feel like I learned anything.
I dont think you need to know (or at least should not need to know) much stats at all to use pre-built libraries like TensorFlow.
It feels to me that a lot of the ML courses around concentrate almost entirely on the stats & maths side of ML though. This strikes me as a bit of mental-masturbation.
To teach people how to program from zero-knowledge, we don't first teach them how modern compilers or the JVM works and how they do their complex optimisations and JIT etc. Why are we teaching people how to use ML from zero-knowledge the absolute raw nuts and bolts of the maths involved (complete with all of the mathematical proofs to prove that something works etc)?
Sure eventually it would be useful to know what is going on with the maths, just like with programming it eventually can be useful to know what the compiler/JVM is really doing, but a LOT of productive stuff can be done when blissfully ignorant of what TensorFlow/the JVM is doing.
ML is easy, but the courses are often too aloof and strike me as academically focused on the maths purely for the sake of the maths itself, rather than on what ML can do. ML is not hard - any programmer can understand it, but the maths is off putting to programmers who are not mathematicians (the majority I'd say)
> Everyone wants to do machine learning, but nobody seems to want to learn statistics.
i think there are are a fair amount of people who want to learn the stats .. even some who want to learn the analysis.
it appears that some things that can be phrased in terms of iterative numerical computation can be difficult because there are probably some properties of the limiting behavior of those computations that can't be learned because they've yet to be discovered.
nonetheless, i (maybe?) get what the parent post is generally saying -- as someone who knows nothing about tensorflow, i wonder if tensorflow users are generally interested in flatness, etc., which (exact sequences of tensor products) is the only guess that i've made about what a portmanteau of "tensor" and "flow" uses as a conceptual model.
i wonder if the difficulty of 'machine learning' is that people tend to approach it as its own thing with its own special, entirely separate bag of tricks. certainly there will be some tricks unique to these iterative statistical techinques.
----
however, i don't think the original article gives enough due-deference to the actual workaday difficulties, challenges, and [non-monetary] rewards of software development in industry: if ML, ANNs, etc. are, as some say, essentially "computer psychology," then being productive on with a team of developers to ship a business product is peddle-to-metal human psychology..
You need to throw some matrix algebra and calc 1 & 2 somewhere in between. Certainly before ESL. It would also require You can't simply read the books and go through the examples. You will be stuck at a concept at many occasions and you will battle it out until after much googling and reading additional papers, you finally get.
Depending on your situation, this advice may be useless, but I'd say for me personally, taking an intro to machine learning class at my university is a wonderful and easy way to learn about the basics. So I'd recommend taking a university class in-person, or online.
